Optimized Pulse Patterns (optimized pulse patterns) may be precomputed patterns of output voltage levels of an electrical converter, which have been optimized with respect to spectral characteristics. The optimization criteria used for computing the optimized pulse pattern may be adapted to a specific application. For machine-connected converters, the objective is typically to reduce the Total Harmonic Distortion (THD) of current or flux, which achieves a motor-friendly operation and prolongs the operating lifetime of the machine. For grid-connected converters, the output current spectrum typically needs to be shaped in order to satisfy grid codes.
The computation method for optimized pulse patterns allows spectrum shaping by employing different cost weights or constraints on different spectral components. Optimized pulse patterns corresponding to different modulation indices are computed offline and stored into a controller memory. During operation of the converter, i.e. online, they are selected for the generation of switching commands based on a modulation index reference, which may be determined from a reference flux, reference voltage or reference current for the converter.
The use of optimized pulse patterns is an established approach for multi-level converter modulation, however, traditional control systems that use optimized pulse patterns usually are limited in performance and flexibility. A typical control system comprises a slow PI loop that determines the modulation index reference, based on which different optimized pulse patterns are read from memory and applied to the system.
Recently, a new method for converter control based on optimized pulse patterns has been proposed, which is based on model predictive control. For example, EP 2 469 692 A1 relates to a method for controlling a converter, in which optimized pulse patterns are modified with model predictive control. The method is based on online adaptation of the switching instances of the optimized pulse patterns. The adaptation of the switching instances is done following the concepts of Model Predictive Control. In EP 2 469 692 A1, the objective of the online control is to track the converter flux reference, provided in αβ coordinates (i.e. it is provided after a two-dimensional Clarke transformation). An outer control loop determines a required αβ flux reference and model predictive control insures that, by appropriately shifting the switching instances, the tracking error is reduced. The method demonstrates very good dynamic performance and thus enables the use of precomputed pulse patterns in dynamically challenging applications.
The objective to track the converter flux in the αβ coordinate system is typical in converter control. For converters connected to the induction machine, the converter flux corresponds to the stator flux of the machine. By controlling the stator flux in αβ coordinates, one can control the machine torque and magnetization. For grid-connected converters, tracking the converter flux may accomplish the control of active and reactive power exchange with the grid.
However, control of the converter flux in αβ is not the sole objective of the converter control. Typically, there is an additional objective related to the energy balancing of the converter. For modular multi-level converters in delta configuration, which are used in STATCOMs and HVDC applications, the additional objective usually is energy balancing of the converter branches, which is achieved by controlling the circulating current. For neutral point clamped converters, a typical objective is balancing of the upper and lower capacitor voltages of the DC link, i.e. the control of the neutral point potential.
However, the control of further quantities, in addition to αβ flux control, is typically done using outer loops and post-processing. Such solutions, which for example include further switching pulses, typically increase the switching frequency, which is highly undesired.
For example, in WO 2013 053 399 A1, circulating currents are controlled for energy balancing of a modular multi-level converter in delta connection.
Also EP 2 667 279 A1 describes a control method for a modular multi-level in a Statcom application, in which a circulating current is controlled.
KOURO S et al: “Model Predictive Control-A Simple and Powerful Method to Control Power Converters”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE SERVICE CENTER, PISCATAWAY, N.J., USA, vol. 56, no. 6, 1 Jun. 2009, pages 1826-1838, relates to FCS-MPC (Finite Control Set Model Predictive Control), in which possible future switching states are determined and a cost function is evaluated on each possible switching state to determine a switching state with a minimal cost function, which is then used as next state applied to a converter. It is mentioned that different variables can be included into the cost function.
JOSE RODRIGUEZ et al: “State of the Art of Finite Control Set Model Predictive Control in Power Electronics”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, IEEE SERVICE CENTER, NEW YORK, N.Y., US, vol. 9, no. 2, 1 May 2013, also relates to FCS-MPC and also mentions that the cost function may be based on a set of different objectives.
FARD RAZIEH NEJATI et al: “Analysis of a Modular Multilevel inverter under the predicted current control based on Finite-Control-Set strategy”, 2013 3RD INTERNATIONAL CONFERENCE ON ELECTRIC POWER AND ENERGY CONVERSION SYSTEMS, IEEE, 2 Oct. 2013, pages 1-6, is a further article relating to FCS-MPC proposing a cost function based on measured and estimated voltages and currents in a converter system.